densenet201
DenseNet-201 convolutional neural network
Syntax
Description
DenseNet-201 is a convolutional neural network that is 201 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 224-by-224. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.
You can use classify
to
classify new images using the DenseNet-201 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with
DenseNet-201.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load DenseNet-201 instead of GoogLeNet.
returns a DenseNet-201
network trained on the ImageNet data set.net
= densenet201
This function requires the Deep Learning Toolbox™ Model for DenseNet-201 Network support package. If this support package is not installed, then the function provides a download link.
returns a DenseNet-201 network trained on the ImageNet data set. This syntax is equivalent
to net
= densenet201('Weights','imagenet'
)net = densenet201
.
returns the untrained DenseNet-201 network architecture. The untrained model does not
require the support package. lgraph
= densenet201('Weights','none'
)
Examples
Output Arguments
References
[1] ImageNet. http://www.image-net.org
[2] Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. "Densely Connected Convolutional Networks." In CVPR, vol. 1, no. 2, p. 3. 2017.
Extended Capabilities
Version History
Introduced in R2018a
See Also
Deep Network Designer | vgg16
| vgg19
| resnet18
| resnet50
| resnet101
| googlenet
| inceptionv3
| inceptionresnetv2
| squeezenet
| trainNetwork
| layerGraph
| DAGNetwork